The Role of Knowledge Graphs and Graph Analytics in Intelligent Policing

by sailforchange | May 20, 2025 | UA at SAIL Blogs

Nour Sakr & Maya Taliha

Introduction

Law enforcement faces increasingly complex challenges, such as crime, fraud, and terrorism, that traditional investigative methods struggle to address. However, integrating advanced technologies like knowledge graphs and graph analytics is revolutionizing policing by connecting disparate data points and uncovering hidden criminal patterns.

Understanding the Power of Connected Data

Data, when linked together, reveals patterns crucial for preventing and solving crimes. Knowledge graphs are structured representations that connect entities—people, places, events—through relationships, allowing investigators to visualize complex networks across multiple data sources. For instance, investigators could link a suspect’s vehicle to phone records, locations, and past interactions, providing a comprehensive view that enhances intelligence gathering.

Examples of Co-offending Network Analysis

The impact of knowledge graphs is evident in co-offending networks. For example, criminals who frequently collaborate are easily identified by visualizing connections, making interventions more strategic. A knowledge graph helps prioritize investigations by distinguishing key players within a network, such as Offender A acting as a bridge between otherwise unconnected criminals.

     

    graphaware.com

    graphaware.com

    Holistic Source Intelligence

    Crime data is often scattered across multiple sources, making integration difficult with traditional methods. Knowledge graphs break down these silos by linking structured and unstructured data from various sources like police reports and surveillance footage. A framework proposed by Qiu & Zhang (2023) uses knowledge graphs to structure multimodal case data and enhance decision-making by breaking down data barriers.

    Knowledge Graphs and the FAIR Principles

    Knowledge graphs also align with the FAIR principles, ensuring data is:

    1. Findable with metadata

       

    2. Accessible to both humans and machines

       

    3. Interoperable across systems

       

    4. Reusable through documented data origins and guideline
      This framework promotes the shift from isolated data silos to an interconnected intelligence system.

    Building a Knowledge Graph for Law Enforcement

    Building a knowledge graph involves three key steps:

    1. Normalization: Standardizing data formats.
    2. Matchmaking: Linking related data points.
    3. Entity Resolution: Eliminating duplicates and resolving ambiguities.

    Once built, these graphs allow investigators to explore relationships and uncover hidden connections.

    graphaware.com

    Using Knowledge Graphs for Crime Solving


     Knowledge graphs facilitate exploration and predictive analysis without requiring advanced technical skills. By querying a graph, investigators can uncover connections, such as identifying all vehicles associated with a victim’s contacts near a crime scene. Furthermore, knowledge graphs support predictive policing by analyzing co-offending networks to anticipate criminal behavior.

    For example, Georgia Tech’s research demonstrates how integrating various data sources into a unified knowledge graph can help detect emerging threats in real-time. Predictive graph algorithms can reveal hidden connections and identify individuals crucial to criminal activities, enabling preemptive actions.

     

     

    graphaware.com

    Application to Lebanon: Addressing Missing Persons Cases

    In conflict and post-conflict scenarios, such as Lebanon, knowledge graphs can help analyze scattered data about missing persons. By connecting testimonies, last-known locations, and government records, they offer insights that could lead to locating missing individuals or providing closure.

    Conclusion

    Knowledge graphs and graph analytics are powerful tools for modern policing, moving beyond traditional methods to harness vast data for uncovering insights and anticipating criminal activities. Their application in areas like missing persons cases holds significant potential for enhancing law enforcement effectiveness worldwide.

    References 

    • Negro, A. (2023). The Role of Knowledge Graphs and Graph Analytics in Intelligent Policing. GraphAware. https://graphaware.com
    • David Barder (Georgia Tech), Predictive Analysis from Massive Knowledge Graphs on Neo4j https://neo4j.com/blog/knowledge-graph/predictive-analysis-from-massive-knowledge-graphs-on-ne
    • Qiu Mingyue, Zhang Xueying Establishment Method of Knowledge Graphs for Public Security Cases. Aut. Control Comp. Sci. 57, 543–551 (2023). https://doi.org/10.3103/S0146411623060056
    • N. Qazi and B. L. W. Wong, “Behavioural & Tempo-Spatial Knowledge Graph for Crime Matching through Graph Theory,” 2017 European Intelligence and Security Informatics Conference (EISIC), Athens, Greece, 2017, pp. 143-146, doi: 10.1109/EISIC.2017.29. keywords: {Data mining;Pattern matching;Visualization;Data visualization;Feature extraction;Search problems;Data mining;associative questioning;data visualization;knowledge graph;Linked Analysis}

    About the Authors: 

    Nour Sakr is an Undergraduate Computer Science Student at AUB

    Maya Taliha is a Computer Science Graduate from AUB